blackHOG [1]}
is a feature descriptor of gradient orientation distributions within local patches.
MaskFeat [2]} finds that HOG-based self-supervised pre-training video model can achieve very good performance in ImageNet.
Therefore, HOG has been studied as a prior to inpainting.
As to [2]}, we utilize a vanilla ViT [4]} to inpaint HOG with masked inputs in RGB channels.
Each cell comprises \(8\times 8\)  pixels with 9 orientations.
Thus a \(256\times 256\)  image results in \(\mathbf {\hat{P}}_h\in \mathbb {R}^{32\times 32\times 27}\)  HOG features. \(L_2\)  loss is used to minimize the distance between the predicted HOG features \(\mathbf {\tilde{P}}_h\)  and the original ones \(\mathbf {\hat{P}}_h\) .
